




'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor, CLIPProcessor





@require_vision
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       tempfile.mkdtemp()

     # fmt: off
     __a								=       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
     # fmt: on
     __a								=       dict(zip(UpperCamelCase   ,				range(len(UpperCamelCase   )   )   )   )
     __a								=       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
     __a								=       {'unk_token': '<unk>'}

     __a								=       os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['vocab_file']   )
     __a								=       os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['merges_file']   )
     with open(self.vocab_file   ,				'w'   ,				encoding='utf-8'   ) as fp:
       fp.write(json.dumps(UpperCamelCase   ) + '\n'   )
     with open(self.merges_file   ,				'w'   ,				encoding='utf-8'   ) as fp:
       fp.write('\n'.join(UpperCamelCase   )   )

     __a								=       {
         'do_resize': True,
         'size': 20,
         'do_center_crop': True,
         'crop_size': 18,
         'do_normalize': True,
         'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
         'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
     }
     __a								=       os.path.join(self.tmpdirname   ,				UpperCamelCase   )
     with open(self.image_processor_file   ,				'w'   ,				encoding='utf-8'   ) as fp:
       json.dump(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     Any:
     return CLIPTokenizer.from_pretrained(self.tmpdirname   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     int:
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     List[str]:
     return CLIPImageProcessor.from_pretrained(self.tmpdirname   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     shutil.rmtree(self.tmpdirname   )







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       [np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta   )]

     __a								=       [Image.fromarray(np.moveaxis(UpperCamelCase   ,				0   ,				-1   )   ) for x in image_inputs]

     return image_inputs







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       self.get_tokenizer()
     __a								=       self.get_rust_tokenizer()
     __a								=       self.get_image_processor()

     __a								=       CLIPProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )
     processor_slow.save_pretrained(self.tmpdirname   )
     __a								=       CLIPProcessor.from_pretrained(self.tmpdirname   ,				use_fast=UpperCamelCase   )

     __a								=       CLIPProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )
     processor_fast.save_pretrained(self.tmpdirname   )
     __a								=       CLIPProcessor.from_pretrained(self.tmpdirname   )

     self.assertEqual(processor_slow.tokenizer.get_vocab()   ,				tokenizer_slow.get_vocab()   )
     self.assertEqual(processor_fast.tokenizer.get_vocab()   ,				tokenizer_fast.get_vocab()   )
     self.assertEqual(tokenizer_slow.get_vocab()   ,				tokenizer_fast.get_vocab()   )
     self.assertIsInstance(processor_slow.tokenizer   ,				UpperCamelCase   )
     self.assertIsInstance(processor_fast.tokenizer   ,				UpperCamelCase   )

     self.assertEqual(processor_slow.image_processor.to_json_string()   ,				image_processor.to_json_string()   )
     self.assertEqual(processor_fast.image_processor.to_json_string()   ,				image_processor.to_json_string()   )
     self.assertIsInstance(processor_slow.image_processor   ,				UpperCamelCase   )
     self.assertIsInstance(processor_fast.image_processor   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       CLIPProcessor(tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()   )
     processor.save_pretrained(self.tmpdirname   )

     __a								=       self.get_tokenizer(bos_token='(BOS)'   ,				eos_token='(EOS)'   )
     __a								=       self.get_image_processor(do_normalize=UpperCamelCase   ,				padding_value=1.0   )

     __a								=       CLIPProcessor.from_pretrained(
         self.tmpdirname   ,				bos_token='(BOS)'   ,				eos_token='(EOS)'   ,				do_normalize=UpperCamelCase   ,				padding_value=1.0   )

     self.assertEqual(processor.tokenizer.get_vocab()   ,				tokenizer_add_kwargs.get_vocab()   )
     self.assertIsInstance(processor.tokenizer   ,				UpperCamelCase   )

     self.assertEqual(processor.image_processor.to_json_string()   ,				image_processor_add_kwargs.to_json_string()   )
     self.assertIsInstance(processor.image_processor   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       self.get_image_processor()
     __a								=       self.get_tokenizer()

     __a								=       CLIPProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )

     __a								=       self.prepare_image_inputs()

     __a								=       image_processor(UpperCamelCase   ,				return_tensors='np'   )
     __a								=       processor(images=UpperCamelCase   ,				return_tensors='np'   )

     for key in input_image_proc.keys():
       self.assertAlmostEqual(input_image_proc[key].sum()   ,				input_processor[key].sum()   ,				delta=1e-2   )







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       self.get_image_processor()
     __a								=       self.get_tokenizer()

     __a								=       CLIPProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )

     __a								=       'lower newer'

     __a								=       processor(text=UpperCamelCase   )

     __a								=       tokenizer(UpperCamelCase   )

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]   ,				encoded_processor[key]   )







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       self.get_image_processor()
     __a								=       self.get_tokenizer()

     __a								=       CLIPProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )

     __a								=       'lower newer'
     __a								=       self.prepare_image_inputs()

     __a								=       processor(text=UpperCamelCase   ,				images=UpperCamelCase   )

     self.assertListEqual(list(inputs.keys()   )   ,				['input_ids', 'attention_mask', 'pixel_values']   )

     # test if it raises when no input is passed
     with pytest.raises(UpperCamelCase   ):
       processor()







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       self.get_image_processor()
     __a								=       self.get_tokenizer()

     __a								=       CLIPProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )

     __a								=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     __a								=       processor.batch_decode(UpperCamelCase   )
     __a								=       tokenizer.batch_decode(UpperCamelCase   )

     self.assertListEqual(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       self.get_image_processor()
     __a								=       self.get_tokenizer()

     __a								=       CLIPProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )

     __a								=       'lower newer'
     __a								=       self.prepare_image_inputs()

     __a								=       processor(text=UpperCamelCase   ,				images=UpperCamelCase   )

     self.assertListEqual(list(inputs.keys()   )   ,				processor.model_input_names   )









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ClapModel",
      "ClapPreTrainedModel",
      "ClapTextModel",
      "ClapTextModelWithProjection",
      "ClapAudioModel",
      "ClapAudioModelWithProjection",
  ]
  UpperCAmelCase_					=   ["ClapFeatureExtractor"]

if TYPE_CHECKING:
  from .configuration_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioConfig,
      ClapConfig,
      ClapTextConfig,
  )
  from .processing_clap import ClapProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_clap import ClapFeatureExtractor
    from .modeling_clap import (
        CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
        ClapAudioModel,
        ClapAudioModelWithProjection,
        ClapModel,
        ClapPreTrainedModel,
        ClapTextModel,
        ClapTextModelWithProjection,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



